NeurIPS 2015spotlight49 citations
Efficient and Parsimonious Agnostic Active Learning
Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire
Abstract
We develop a new active learning algorithm for the streaming settingsatisfying three important properties: 1) It provably works for anyclassifier representation and classification problem including thosewith severe noise. 2) It is efficiently implementable with an ERMoracle. 3) It is more aggressive than all previous approachessatisfying 1 and 2. To do this, we create an algorithm based on a newlydefined optimization problem and analyze it. We also conduct the firstexperimental analysis of all efficient agnostic active learningalgorithms, evaluating their strengths and weaknesses in differentsettings.
BibTeX
@inproceedings{NIPS2015_0d4f4805,
author = {Huang, Tzu-Kuo and Agarwal, Alekh and Hsu, Daniel J and Langford, John and Schapire, Robert E},
booktitle = {Advances in Neural Information Processing Systems},
editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Efficient and Parsimonious Agnostic Active Learning},
url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/0d4f4805c36dc6853edfa4c7e1638b48-Paper.pdf},
volume = {28},
year = {2015}
}